Introduction to Reinforcement Learning

author:Csaba Szepesvari, Department of Computing Science, University of Alberta
published: March 17, 2008,   recorded: March 2008,   views: 863
Categories
You might be experiencing some problems with Your Video player.

Related content

Visitors who watched this lecture also watched...
04:58:57
Reinforcement Learning

1386 views - Satinder Singh, 2006
19:38
Introduction to Reinforcement Learning and Bayesian learning

1009 views - Mohammad Ghavamzadeh, 2007
02:15:42
Reinforcement Learning Theory

902 views - John Langford, 2006
05:22:53
Monte Carlo Simulation for Statistical Inference, Model Selection and Decision Making

8258 views - Nando de Freitas, 2008
02:25:53
Introduction to Statistical Machine Learning

1908 views - Marcus Hutter, 2008
05:09:55
Reinforcement learning

259 views - Scott Sanner, 2009
05:09:25
Reinforcement Learning

288 views - Peter L. Bartlett, 2002
05:12:02
Kernel methods and Support Vector Machines

3841 views - Alexander J. Smola, 2008
01:20:19
Policy-gradient Reinforcement Learning

435 views - Douglas Aberdeen, 2006
02:56:12
Foundations of Machine Learning

686 views - Marcus Hutter, 2008

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 0:57:16 Flash video Windows Media video
!NOW PLAYING
Watch Part 2
Part 2 0:58:11 Flash video Windows Media video
Watch Part 3
Part 3 0:52:52 Flash video Windows Media video
Watch Part 4
Part 4 1:02:00 Flash video Windows Media video
Watch Part 5
Part 5 0:57:32 Flash video Windows Media video
Watch Part 6
Part 6 0:59:47 Flash video Windows Media video

Description

The tutorial will introduce Reinforcement Learning, that is, learning what actions to take, and when to take them, so as to optimize long-term performance. This may involve sacrificing immediate reward to obtain greater reward in the long-term or just to obtain more information about the environment. The first part of the tutorial will cover the basics, such as Markov decision processes, dynamic programming, temporal-difference learning, Monte Carlo methods, eligibility traces, the role of function approximation. In the second part we cover some recent developments, namely policy gradient and second order methods, such as LSPI and the modified Bellman residual minimization algorithm.

Link this page  

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: